Robust people detection by fusion of evidence from multiple methods

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. V. Fernández-Carbajales, M. A. García, and J. M. Martínez, "Robust People Detection by Fusion of Evidence from Multiple Methods", in Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008. WIAMIS 2008, p. 55-58.This paper describes and evaluates an algorithm for real-time people detection in video sequences based on the fusion of evidence provided by three simple independent people detectors. Experiments with real video sequences show that the proposed integration-based approach is effective, robust and fast by combining simple algorithms.This work is supported by Cátedra Infoglobal-UAM para “Nuevas tecnologías de vídeo aplicadas a la seguridad”, the Spanish Government (TEC2007- 65400 SemanticVideo) and the Comunidad de Madrid (S-050/TIC-0223 - ProMultiDis-CM)

Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic : a matched analysis

The primary aim of our study was to investigate the association between intubation timing and hospital mortality in critically ill patients with COVID-19-associated respiratory failure. We also analysed both the impact of such timing throughout the first four pandemic waves and the influence of prior non-invasive respiratory support on outcomes. This is a secondary analysis of a multicentre, observational and prospective cohort study that included all consecutive patients undergoing invasive mechanical ventilation due to COVID-19 from across 58 Spanish intensive care units (ICU) participating in the CIBERESUCICOVID project. The study period was between 29 February 2020 and 31 August 2021. Early intubation was defined as that occurring within the first 24 h of intensive care unit (ICU) admission. Propensity score (PS) matching was used to achieve balance across baseline variables between the early intubation cohort and those patients who were intubated after the first 24 h of ICU admission. Differences in outcomes between early and delayed intubation were also assessed. We performed sensitivity analyses to consider a different timepoint (48 h from ICU admission) for early and delayed intubation. Of the 2725 patients who received invasive mechanical ventilation, a total of 614 matched patients were included in the analysis (307 for each group). In the unmatched population, there were no differences in mortality between the early and delayed groups. After PS matching, patients with delayed intubation presented higher hospital mortality (27.3% versus 37.1%, p =0.01), ICU mortality (25.7% versus 36.1%, p=0.007) and 90-day mortality (30.9% versus 40.2%, p=0.02) when compared to the early intubation group. Very similar findings were observed when we used a 48-hour timepoint for early or delayed intubation. The use of early intubation decreased after the first wave of the pandemic (72%, 49%, 46% and 45% in the first, second, third and fourth wave, respectively; first versus second, third and fourth waves p<0.001). In both the main and sensitivity analyses, hospital mortality was lower in patients receiving high-flow nasal cannula (n=294) who were intubated earlier. The subgroup of patients undergoing NIV (n=214) before intubation showed higher mortality when delayed intubation was set as that occurring after 48 h from ICU admission, but not when after 24 h. In patients with COVID-19 requiring invasive mechanical ventilation, delayed intubation was associated with a higher risk of hospital mortality. The use of early intubation significantly decreased throughout the course of the pandemic. Benefits of such an approach occurred more notably in patients who had received high-flow nasal cannul

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

Introduction: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. Methods: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. Results: The median [p25–p75] time from discharge to follow-up was 3.57 [2.77–4.92] months. Median age was 60 [53–67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO < 80% and 24% having DLCO < 60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO < 60% were chronic lung disease (CLD) (OR: 1.86 (1.18–2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37–1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18–1.63)), urea (OR: 1.16 (0.97–1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73–1.06)). Bacterial pneumonia (1.62 (1.11–2.35)) and duration of ventilation (NIMV (1.23 (1.06–1.42), IMV (1.21 (1.01–1.45)) and prone positioning (1.17 (0.98–1.39)) were associated with fibrotic lesions. Conclusion: Age and CLD, reflecting patients’ baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Atención visual basada en un espacio perceptual conjunto de color y brillo para la mejora de algoritmos de seguimiento de objetos en secuencias de vídeo

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 20-07-2018Esta tesis doctoral realiza una revisión en profundidad de los actuales modelos de atención visual basados en enfoques biológicos, con el fin de proponer un nuevo modelo de atención visual basado en un espacio perceptual conjunto de color y brillo. Además, se demuestra que este modelo es capaz de extraer características visuales más discriminantes, especialmente cuando se trata de objetos visualmente muy similares. Ese espacio conjunto de color y brillo se basa en un modelo perceptual teórico de inspiración biológica, originalmente propuesto por Izmailov y Sokolov en el ámbito de la psicofísica. La presente tesis propone un modelo computacional que permite la aplicación del modelo teórico de Izmailov y Sokolov en imágenes digitales, ya que el modelo original sólo puede aplicarse a datos perceptuales directamente extraídos de experimentos psicofísicos. Los resultados experimentales con secuencias de vídeo reales demuestran que el modelo de atención visual propuesto produce resultados significativamente más precisos en el ámbito particular del seguimiento en secuencias de vídeo que los modelos de atención visual actuales, los cuales procesan el color y el brillo por separado. Palabras clave: Modelos de atención visual, Espacios perceptuales, Seguimiento, Color y Brillo.This doctoral thesis performs an in-depth review of current visual attention models based on biological approaches to propose a new visual attention model based on a joint perceptual space of both colour and brightness, and shows that this model is able to extract more discriminant visual features, especially when dealing with objects that are very similar visually. That joint colour and brightness space is based on a biologically-inspired theoretical perceptual model originally proposed by Izmailov and Sokolov in the scope of psychophysics. The present thesis proposes a computational model that allows the application of Izmailov and Sokolov’s theoretical model to digital images, since the original model can only be applied to perceptual data directly drawn from psychophysical experiments. Experimental results with real video sequences show that the proposed visual attention model yields significantly more accurate results in the particular application scope of video tracking than well-known visual attention models that process colour and brightness separately. Keywords: Visual attention models, Perceptual spaces, Tracking, Colour and Brightness

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Abstract. This paper presents a proposal for description tools, following the MPEG-7 standard, for the high-level description of humanoids. Given the almost complete lack of high-level description tools for 3D graphics content in the current MPEG-7 specification, we propose descriptions aimed at describing virtual humanoids, both for indexing and query support (no extraction tools are presented here), and also for the generation of personalized humanoids using high-level descriptions via a simple GUI instead of complex authoring tools. This later application, which is the focus of the work presented here, is related with the Authoring 744 initiative that targets the creation of content from descriptions that are authored in a user friendly (natural) way. This work is under development within the EU-funded research project OLGA, where the description tools should provide the means for the creation and modification of humanoids inside an on-line 3D gaming environment, but our description tools are generic enough to be used in the future in many different applications: robot portrait, indexing/searching, etc.